数字信号处理（第二版）（英文版）pdf/txt下载_在线阅读全文

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内容简介

本书在Richard G. Lyons所著Understanding Digital Signal Processing, Second Edition的基础上进行了改编，针对通信类学校本科教学大纲，删除了教学中一般不涉及的内容，调整了章节顺序，并增加了z反变换、滤波器结构、线性相位FIR滤波器和其结构、模拟滤波器简介的内容，使教内容材更加完整。全书在概述了离散序列和系统的定义和实例之后，详细讨论了离散系统的特性、信号的离散化和离散卷积、z变换、离散时间傅里叶变换和离散傅里叶变换、快速傅里叶变换、数字滤波器结构、以及有限和无限脉冲响应数字滤波器的设计等基本概念和基本理论。书中涉及的数学知识以简明形式给出，深入浅出，易于理解。本书每章都增加了例题、习题和MATLAB例题，以便加强对每章内容的理解和掌握。

作者简介

Richard G. Lyons毕业于美国加州阿克隆大学，曾任美国国家安全局和TRW（天合）汽车集团公司众多DSP系统的首席硬件工程师，负责系统设计、开发、测试和安装。曾任教于加利福尼亚大学塔克鲁兹校区，并发表过很多有关DSP的文章。作为IEEE Signal Processing Magazine的副主编，开设了DSP Tips & Tricks专栏并负责编辑。 Richard G. Lyons毕业于美国加州阿克隆大学，曾任美国国家安全局和TRW（天合）汽车集团公司众多DSP系统的首席硬件工程师，负责系统设计、开发、测试和安装。曾任教于加利福尼亚大学塔克鲁兹校区，并发表过很多有关DSP的文章。作为IEEE Signal Processing Magazine的副主编，开设了DSP Tips & Tricks专栏并负责编辑。

目　　录

Chapter 1 Discrete Sequences and Systems 1
1.1 Discrete Sequences and Their Notation 1
1.1.1 Discrete-time Signals 2
1.1.2 Frequently Used Discrete Sequences 7
1.2 Signal Amplitude, Magnitude, Power 9
1.3 Signal Processing Operational Symbols 10
1.4 Introduction to Discrete Linear Time-Invariant Systems 12
1.5 Discrete Linear Systems 12
1.5.1 Example of a Linear System 12
1.5.2 Example of a Nonlinear System 14
1.6 Time-Invariant Systems 16
1.6.1 Example of a Time-Invariant System 16
1.7 The Commutative Property of Linear Time-Invariant Systems 17
1.8 The Causality Property of Linear Time-Invariant Systems 18

Chapter 1 Discrete Sequences and Systems	1 1.1 Discrete Sequences and Their Notation	1 1.1.1 Discrete-time Signals	2 1.1.2 Frequently Used Discrete Sequences	7 1.2 Signal Amplitude, Magnitude, Power	9 1.3 Signal Processing Operational Symbols	10 1.4 Introduction to Discrete Linear Time-Invariant Systems	12 1.5 Discrete Linear Systems	12 1.5.1 Example of a Linear System	12 1.5.2 Example of a Nonlinear System	14 1.6 Time-Invariant Systems	16 1.6.1 Example of a Time-Invariant System	16 1.7 The Commutative Property of Linear Time-Invariant Systems	17 1.8 The Causality Property of Linear Time-Invariant Systems	18 1.9 The Stability Property of Linear Time-Invariant Systems	18 1.10 Analyzing Linear Time-Invariant Systems	19 1.11 Problems	20 1.12 MATLAB Applications	22 1.12.1 Applications	22 1.12.2 Examples	23 1.12.3 Exercises	24 Chapter 2 Periodic Sampling	25 2.1 Aliasing: Signal Ambiguity in the Frequency Domain	25 2.2 Sampling Low-Pass Signals	29 2.3 A Generic Description of Discrete Convolution	32 2.3.1 Discrete Convolution in the Time Domain	33 2.3.2 The Convolution Theorem	36 2.3.3 Applying the Convolution Theorem	39 2.4 Problems	42 2.5 Matlab Applications	43 2.5.1 Applications	43 2.5.2 Examples	44 2.5.3 Exercises	46 Chapter 3 Z-Transform	47 3.1 The z-Transform	47 3.1.1 Poles and Zeros on the z-Plane and Stability	49 3.1.2 The ROC of z-Transform	50 3.1.3 The Properties of z-Transform	53 3.2 The Inverse z-Transform	55 3.2.1 General Expression of Inverse z-Transform	55 3.2.2 Inverse z-Transform by Partial-Fraction Expansion	58 3.3 Problems	61 3.4 MATLAB Applications	63 3.4.1 Applications	63 3.4.2 Examples	64 3.4.3 Exercises	67 Chapter 4 The Discrete Fourier Transform	68 4.1 Interpreting the DFT	69 4.2 Understanding the DFT Equation	71 4.2.1 DFT Example 1	74 4.3 DFT Properties	81 4.3.1 DFT Symmetry	81 4.3.2 DFT Linearity	83 4.3.3 DFT Magnitudes	83 4.3.4 DFT Frequency Axis	84 4.3.5 DFT Shifting Theorem	85 4.4 Inverse DFT	87 4.5 DFT Leakage	88 4.6 Windows	94 4.7 DFT Resolution, Zero Padding, and Frequency-Domain Sampling	101 4.8 Frequency Response	104 4.9 Problems	106 4.10 Matlab Applications	107 4.10.1 Applications	107 4.10.2 Examples	108 4.10.3 Exercises	111 Chapter 5 The Fast Fourier Transform	112 5.1 Relationship of the FFT to the DFT	112 5.2 FFT Alogrithm	113 5.3 Derivation of the Radix-2 FFT Algorithm	114 5.4 FFT Input/Output Data Index Bit Reversal	120 5.5 Radix-2 FFT Butterfly Structures	121 5.6 Efficiently Performing the FFT of Real Sequences	127 5.6.1 Performing Two N-Point Real FFTs	127 5.6.2 Performing a 2N-Point Real FFT	133 5.7 Discrete Convolution using FFT	137 5.7.1 Overlap-added	138 5.7.2 Overlap-saved	138 5.8 IFFT Algorithm	140 5.9 Problems	143 5.10 Matlab Applications	144 5.10.1 Applications	144 5.10.2 Examples	144 5.10.3 Exercises	146 Chapter 6 Filter Structure	147 6.1 Block Structure	147 6.2 Mason and Transpose Theorem	149 6.2.1 Mason?s Rule	149 6.2.2 Transpose Theorem	150 6.3 Example of Filter Structures	151 6.3.1 IIR Filter Structure	151 6.3.2 FIR Direct Structure	158 6.3.3 FIR Cascade Structure	158 6.4 Problems	159 6.5 Matlab Applications	162 6.5.1 Applications	162 6.5.2 Examples	163 6.5.3 Exercises	167 Chapter 7 Finite Impulse Response Filters	168 7.1 An Introduction to Finite Impulse Response (FIR) Filters	169 7.2 Properties of FIR Filters	172 7.2.1 Convolution in FIR Filters	172 7.2.2 Linear phase FIR Filter	181 7.2.3 Linear Phase FIR Filter Structure	188 7.2.4 FIR Filter Poles and Zeros	194 7.3 Low-Pass FIR Filter Design	196 7.3.1 Window Design Method	197 7.3.2 Windows Used in FIR Filter Design	204 7.3.3 Examples to Design Linear Phase Low-Pass FIR Filter	210 7.4 Examples to Design Other Types Linear Phase FIR Filter	215 7.5 Problems	220 7.6 Matlab Exercises	223 7.6.1 Applications	223 7.6.2 Examples	223 7.6.3 Exercises	224 Chapter 8 Infinite Impulse Response Filters	226 8.1 An Introduction to Infinite Impulse Response Filters	227 8.2 The Laplace Transform	229 8.2.1 Poles and Zeros on the s-Plane and Stability	234 8.3 Analog Low-Pass Filters	239 8.3.1 Introduction	240 8.3.2 Approximation of analog filter characteristics	240 8.3.3 Butterworth Approximation	242 8.3.4 Chebyshev Approximation	247 8.4 Impulse Invariance IIR Filter Design Method	253 8.4.1 Impulse Invariance Design Method 1 Example	258 8.4.2 Impulse Invariance Design Method 2 Example	260 8.5 Bilinear Transform IIR Filter Design Method	266 8.5.1 Bilinear Transform Design Example	271 8.6 Low-Pass IIR Filter Design	274 8.6.1 Example of Low-Pass IIR Digital Filter Design	274 8.6.2 a Brief Comparison of IIR and FIR Filters	277 8.7 Other Types IIR Filter Design	278 8.8 Problems	284 8.9 Matlab Exercises	286 8.9.1 Functions of IIR Design 	286 8.9.2 Examples	287 8.9.3 Exercises	288

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前　　言

本书特色
◆ 强调结合工程应用的DSP例子并给出解决思路
◆ 丰富了z变换、DRFT变换等DSP重要内容
◆ 增加了习题和DSP编程的内容
◆ 通俗、易于理解的语言风格和内容
◆ 丰富、直观的大量DSP例题和方案
◆ 给出了滤波器结构的实现手段

本书特色 ◆ 强调结合工程应用的DSP例子并给出解决思路 ◆ 丰富了z变换、DRFT变换等DSP重要内容 ◆ 增加了习题和DSP编程的内容 ◆ 通俗、易于理解的语言风格和内容 ◆ 丰富、直观的大量DSP例题和方案 ◆ 给出了滤波器结构的实现手段
改 编 者 序
本书是在Understanding Digital Signal Processing, Second Edition的基础上改编的英文版教材，适合数字信号处理（DSP）基础相关课程40学时到64学时的英语或双语教学使用。 本书的作者Richard G. Lyons毕业于美国加州阿克隆大学。他曾是美国国家安全局和TRW（天合）汽车集团公司众多DSP系统的首席硬件工程师，负责系统设计、开发、测试和安装。他曾在加利福尼亚大学圣塔克鲁兹分校任教，并发表过很多有关DSP的文章。作为IEEE Signal Processing Magazine的副主编，由他开设了DSP Tips ＆ Tricks专栏并且负责编辑。正是由于其在DSP领域的长期工作经验和教学经历，编写的此书受到了很多数字信号处理初学者和使用者的高度评价，认为此书将“理论与实践完美地结合起来”。由于数字信号处理是一门应用性很强的课程，需要大量的经验积累，而此书通过具有启发性的解释和精心挑选的例子，采用读者可以理解的数学表示方法，对数字信号处理技术进行了解释，帮助读者从整体上掌握DSP，并逐步掌握较高层次的DSP概念和应用。这也是改编者选择此书的初衷，希望读者不仅了解DSP的概念，而且通过例子的学习，在今后的工作和科研工作中，会用DSP的知识解决实际问题。 Understanding Digital Signal Processing, Second Edition的内容体系与国内高校数字信号处理基础相关课程的教学内容有较高的统一性，但是，通过亲自实践以及进一步考察国内高校相关学科开展双语教学的情况，也发现本书与国内高校的教学内容和课程安排存在一定不匹配之处。自国内高校教育改革以来，大部分院校都开设了DSP课程，而且内容也基本形成了一致，而本书的内容相对国内的授课学时和知识体系显得内容过于庞大。例如，关于信号量化的内容在国内高等院校的授课中通常是介绍性的，而原书用了整章的篇幅（第8章）来详细介绍；关于离散希尔伯特变换、信号平均和数字信号格式等内容，通常在国内数字信号处理基础课程教学中也很少涉及，或者属于只要求学生了解的内容。但对于数字信号处理使用的常用方法，MATLAB在国内一般都会作为数字信号处理实验的内容来介绍，但本书却没有涉及。 为了使教材内容更合理，范围宽窄适度，内容深浅适中，进而满足国内和国际高等教育相关专业数字信号处理课程的教学要求，适应国内教育教学特色，并便于学生在有限学时下把握重点，深入理解，而且学以致用，我们以精简内容和突出重点为目标对这本书进行了改编。删除了教学中一般不涉及的内容，调整了章节顺序，并增加了z反变换、滤波器结构、线性相位FIR滤波器及其结构、模拟滤波器简介等内容，使教材内容更加完整。全书在概述了离散序列和系统的定义及实例之后，详细讨论了离散系统的特性、信号的离散化和离散卷积、z变换、离散时间傅里叶变换和离散傅里叶变换、快速傅里叶变换、数字滤波器结构及有限和无限脉冲响应数字滤波器的设计等基本概念和基本理论。书中涉及的数学知识以简明形式给出，深入浅出，易于理解。本书每章都增加了例题、习题和MATLAB例题，便于加强对每章内容的理解和掌握。本书可作为理工类大专院校的电子、计算机、通信等专业的本科生教材，对于DSP领域的工程技术人员和专业技术人员也有很好的参考价值。 本书第1章至第3章由许晓东教师负责改编，第4章至第6章由孙松林老师负责改编，第7章和第8章由张建华老师负责改编，全书由张建华老师负责统校。在本书的改编过程中，一方面结合了改编者们在北京邮电大学国际学院开展数字信号处理双语授课的实践经验（教学用PPT等教辅资源可联系<a href="mailto:malan@phei.com.cn">malan@phei.com.cn</a>申请），另一方面，北京邮电大学门爱东教授、苏菲教授和已退休的周利清教授都给予我们许多帮助和指导，在此表示衷心感谢！最后感谢热心的国际学院已毕业的学生王禹宁和闫  等对本书的关注，感谢许多无法一一提及的支持此书的老师和同学！ Preface This book is an expansion of the original Understanding Digital Signal Processing textbook published in 1997 and, like the first edition, its goal is to help beginners understand this relatively new technology of digital signal processing (DSP). Additions to this second edition include:
● Expansion and clarification of selected spectrum analysis and digital filtering topics covered in the first edition making that material more valuable to the DSP beginner. ● Discussions of Frequency Sampling, Interpolated FIR, and CIC filters; giving these important filters greater exposure than they‘re received in past DSP textbooks. ● Revision of the terminology making it more consistent with the modern day language of DSP. 
It誷 traditional at this point in the preface of a DSP textbook for the author to tell readers why they should learn DSP. I don＇t need to tell you how important DSP is in our modern engineering world, you already know that. I＇ll just say that the future of electronics is DSP, and with this book you will not be left behind. LEARNING DIGITAL SIGNAL PROCESSING Learning the fundamentals, and how to speak the language, of digital signal processing does not require profound analytical skills or an extensive background in mathematics. All you need is a little experience with elementary algebra, knowledge of what a sinewave is, this book, and enthusiasm. This may sound hard to believe, particularly if you＇re just flipped through the pages of this book and seen figures and equations that look rather complicated. The content here, you say, looks suspiciously like the material in technical journals and textbooks that, in the past, have successfully resisted your attempts to understand. Well, this is not just another book on digital signal processing. This book＇s goal is to gently provide explanation followed by illustration, not so that you may understand the material, but that you must understand the material. ① Remember the first time you saw two people playing chess? The game probably appeared to be mysterious and confusing. As you now know, no individual chess move is complicated. Given a little patience, the various chess moves are easy to learn. The games complexity comes from deciding what combinations of moves to make and when to make them. So it is wi

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